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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 October 2021

Utilizing Genotypic Information as a Prior for Learning Gene Networks

Utilizing Genotypic Information as a Prior for Learning Gene Networks

(p.149) Chapter 6 Utilizing Genotypic Information as a Prior for Learning Gene Networks
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Kyle Chipman

Ambuj Singh

Oxford University Press

The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. Leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. However, one major drawback of these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. This chapter reviews two methods where all interactions between genotype and gene transcripts are considered collectively in order to better resolve causal relationships between gene transcripts. The likelihood-based causality model selection (LCMS) of Schadt and collaborators is first described. Then, the stochastic causal tree (SCT) method is depicted. The information provided by such methods is intended to be used as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.

Keywords:   structure learning, causality model selection, Bayesian network

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